A major challenge during mechanical ventilation of patients in the intensive care unit (ICU) is how to synchronize the ventilator with the patient's breathing effort smoothly and effectively. Dyssynchrony could lead to patient discomfort and increased need for sedation, longer hospital stay and lower probability of survival. Current mechanical ventilator designs for use in the ICU do not take into consideration the patient's profound respiratory adaptation to vagal volume-related feedback, and are prone to patient-ventilator asynchrony. Understanding the bidirectional relationship between control of breathing and mechanical ventilation and bringing such new concepts to the bedside is increasingly recognized as a major unmet priority in critical care medicine. Entrainment-based mechanical ventilation is a novel ventilation approach that may potentially revolutionize the field and shift clinical practice by incorporating respiratory neurobiology concepts into mechanical ventilator designs. This innovative mechanical ventilation technique is motivated by recent evidence indicating that neural circuits in the pontine pneumotaxic center plays an important role in promoting respiratory entrainment to mechanical ventilation through learning and memory of the Hering-Breuer reflex. Patient-ventilator entrainment is a fundamental physiologic phenomenon that is grounded in the classical physics theory of mutual entrainment between coupled oscillators. In entrainment-based ventilation, the patient's spontaneous respiratory rhythm and the ventilator rhythm are phase-locked to one another on the same tempo, just like two individuals dancing together. This new-generation ventilation mode has recently gained FDA approval of investigational device exemption for clinical trial. To facilitate the translation of te base technology from the benchtop to the bedside, this R01 application proposes a series of preclinical studies with an objective to gain better understanding of the neurophysiologic determinants of patient-ventilator interaction. Because patient-ventilator entrainment is a complex phenomenon, a systems biology approach combining experimental testing and multiscale modeling for quantitative data analysis and prediction of novel outcomes is essential. This will be achieved by elucidating the central mechanisms underlying respiratory adaptation to mechanical ventilation at both the systems (Aim 1) and cellular levels (Aim 2), and developing a multiscale neural network model of patient-ventilator interaction in order to simulate the experimental results and predict the open-loop and closed-loop respiratory-ventilator entrainment frequency and phase response relationships in a quantitative manner (Aim 3). A major hypothesis to be tested is that respiratory entrainment to mechanical ventilation is mediated by a distinct neuronal population in the pontine pneumotaxic center which is distinguished by its critical dependence on NMDA receptor activity and sensitivity to inhibition by the neuropeptide somatostatin.